A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

Toyosi Toriola Ademujimi, Michael P. Brundage, Vittaldas V. Prabhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learning algorithms. Papers published from 2007 to 2017 were reviewed and keywords were used to identify 20 articles spanning the most prominent machine learning algorithms. Most articles reviewed consisted of training data obtained from sensors attached to the equipment. The training of the machine learning algorithm consisted of designed experiments to simulate different faulty and normal processing conditions. The areas of application varied from wear of cutting tool in computer numeric control (CNC) machine, surface roughness fault, to wafer etching process in semiconductor manufacturing. In all cases, high fault classification rates were obtained. As the interest in smart manufacturing increases, this review serves to address one of the cornerstones of emerging production systems.

Original languageEnglish (US)
Title of host publicationAdvances in Production Management Systems
Subtitle of host publicationThe Path to Intelligent, Collaborative and Sustainable Manufacturing - IFIP WG 5.7 International Conference, APMS 2017, Proceedings
EditorsRalph Riedel, Klaus-Dieter Thoben, Dimitris Kiritsis, Gregor von Cieminski, Hermann Lodding
PublisherSpringer New York LLC
Pages407-415
Number of pages9
ISBN (Print)9783319669229
DOIs
StatePublished - Jan 1 2017
EventIFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2017 - Hamburg, Germany
Duration: Sep 3 2017Sep 7 2017

Publication series

NameIFIP Advances in Information and Communication Technology
Volume513
ISSN (Print)1868-4238

Other

OtherIFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2017
CountryGermany
CityHamburg
Period9/3/179/7/17

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management

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    Ademujimi, T. T., Brundage, M. P., & Prabhu, V. V. (2017). A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis. In R. Riedel, K-D. Thoben, D. Kiritsis, G. von Cieminski, & H. Lodding (Eds.), Advances in Production Management Systems: The Path to Intelligent, Collaborative and Sustainable Manufacturing - IFIP WG 5.7 International Conference, APMS 2017, Proceedings (pp. 407-415). (IFIP Advances in Information and Communication Technology; Vol. 513). Springer New York LLC. https://doi.org/10.1007/978-3-319-66923-6_48